Update README.md

README.md

@@ -1,447 +1 @@
----
-library_name: sentence-transformers
-metrics:
-- negative_mse
-pipeline_tag: sentence-similarity
-tags:
-- sentence-transformers
-- sentence-similarity
-- feature-extraction
-- generated_from_trainer
-- dataset_size:25095
-- loss:MSELoss
-widget:
-- source_sentence: mariknak pay ketdi a naabrasaak iti kulonganda
-  sentences:
-  - Nakuha nako ang usa ka kuptanan sa istorya ug nagsugod kini sa pagbati ug porma
-    nga akong gusto
-  - 'Ang kasarangang pag-ulan sa London, nga adunay kataas nga 10°C ug ang ubos nga
-    6°C. #LondonWeather #RainyDay'
-  - Controversial religious text causes uproar among community members
-- source_sentence: "JUAN COLE: Ang Pagduso sa Islamic State sa Baghdad 'Usa ka\
-    \ Pagsulay Aron Mabawi ang Gikuha sa Bush Administration' \n"
-  sentences:
-  - Ang Touchdown nga Selebrasyon ni Antonio Brown Sexy Gihapon Alang sa NFL Bisan
-    ang duha ka pagduso makapasilo kanimo.
-  - Natuklasan ng mga siyentipiko ang mga bagong species ng nilalang sa malalim na
-    dagat
-  - i feel so glad doing this
-- source_sentence: New Curriculum Standards to Be Implemented in All Schools Next
-    Year
-  sentences:
-  - "Climate Change This Week: Mega Methane, Tidal Power, and More \n"
-  - '@lilomatic Only in Zimbabwe where u find Opposition party for another Opposition
-    party.'
-  - "Ang mamumuno nga si Mike namulong sa Ferguson: 'Ang Hustisya Dili Kanunay\
-    \ Gisilbi' \n"
-- source_sentence: i am so blessed and feel blessed to be able to share my creations
-    with you
-  sentences:
-  - "Ania ang Buhaton Sa World Cup Host Cities Gawas sa Pagtan-aw sa Soccer \n"
-  - "Hillary Clinton's 'Super Volunteers' Are Back And Ready For 2016 \n"
-  - Awan pay ti koriente para kadagiti paset ti Joburg kalpasan ti uram ti kable iti
-    uneg ti daga https://t.co/szuZa380Lr
-- source_sentence: "3 Napateg nga Addang (iti Aniaman nga Edad) tapno Agsagana iti\
-    \ Matay \n"
-  sentences:
-  - EPIC! RAND PAUL Laughs at CNN’s Climate Hysteria…Schools Jake Tapper on Climate
-    Truth [Video]
-  - im feeling horrible
-  - 'Image: WC Provincial Disaster Management Centre https://t.co/EcNgpBhjcV'
-model-index:
-- name: SentenceTransformer
-  results:
-  - task:
-      type: knowledge-distillation
-      name: Knowledge Distillation
-    dataset:
-      name: Unknown
-      type: unknown
-    metrics:
-    - type: negative_mse
-      value: -0.2521140966564417
-      name: Negative Mse
----
-
-# SentenceTransformer
-
-This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
-
-## Model Details
-
-### Model Description
-- **Model Type:** Sentence Transformer
-<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
-- **Maximum Sequence Length:** 128 tokens
-- **Output Dimensionality:** 768 tokens
-- **Similarity Function:** Cosine Similarity
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
-
-### Model Sources
-
-- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
-
-### Full Model Architecture
-
-```
-SentenceTransformer(
-  (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
-  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-)
-```
-
-## Usage
-
-### Direct Usage (Sentence Transformers)
-
-First install the Sentence Transformers library:
-
-```bash
-pip install -U sentence-transformers
-```
-
-Then you can load this model and run inference.
-```python
-from sentence_transformers import SentenceTransformer
-
-# Download from the 🤗 Hub
-model = SentenceTransformer("sentence_transformers_model_id")
-# Run inference
-sentences = [
-    '3 Napateg nga Addang (iti Aniaman nga Edad) tapno Agsagana iti Matay \n',
-    'EPIC! RAND PAUL Laughs at CNN’s Climate Hysteria…Schools Jake Tapper on Climate Truth [Video]',
-    'Image: WC Provincial Disaster Management Centre https://t.co/EcNgpBhjcV',
-]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 768]
-
-# Get the similarity scores for the embeddings
-similarities = model.similarity(embeddings, embeddings)
-print(similarities.shape)
-# [3, 3]
-```
-
-<!--
-### Direct Usage (Transformers)
-
-<details><summary>Click to see the direct usage in Transformers</summary>
-
-</details>
--->
-
-<!--
-### Downstream Usage (Sentence Transformers)
-
-You can finetune this model on your own dataset.
-
-<details><summary>Click to expand</summary>
-
-</details>
--->
-
-<!--
-### Out-of-Scope Use
-
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
-
-## Evaluation
-
-### Metrics
-
-#### Knowledge Distillation
-
-* Evaluated with [<code>MSEEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.MSEEvaluator)
-
-| Metric           | Value       |
-|:-----------------|:------------|
-| **negative_mse** | **-0.2521** |
-
-<!--
-## Bias, Risks and Limitations
-
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
-
-<!--
-### Recommendations
-
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
-
-## Training Details
-
-### Training Dataset
-
-#### Unnamed Dataset
-
-
-* Size: 25,095 training samples
-* Columns: <code>sentence_0</code> and <code>label</code>
-* Approximate statistics based on the first 1000 samples:
-  |         | sentence_0                                                                        | label                                |
-  |:--------|:----------------------------------------------------------------------------------|:-------------------------------------|
-  | type    | string                                                                            | list                                 |
-  | details | <ul><li>min: 4 tokens</li><li>mean: 23.49 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>size: 768 elements</li></ul> |
-* Samples:
-  | sentence_0                                                                                | label                                                                                                                             |
-  |:------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------|
-  | <code>A suicide bomber targeting a crowded market resulting in numerous fatalities</code> | <code>[-0.05337272211909294, -0.296869158744812, -0.005234384443610907, -0.017071111127734184, 0.01954558491706848, ...]</code>   |
-  | <code>Jeb Bush To Meet With Charleston Pastors <br></code>                                | <code>[-0.025684779509902, 0.2293000966310501, -0.005389949772506952, 0.09448838979005814, 0.017471183091402054, ...]</code>      |
-  | <code>New scientific research suggests link between air pollution and lung disease</code> | <code>[-0.12967786192893982, 0.19541345536708832, -0.0044404976069927216, -0.06291326135396957, -0.03776596114039421, ...]</code> |
-* Loss: [<code>MSELoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#mseloss)
-
-### Training Hyperparameters
-#### Non-Default Hyperparameters
-
-- `eval_strategy`: steps
-- `per_device_train_batch_size`: 64
-- `per_device_eval_batch_size`: 64
-- `num_train_epochs`: 20
-- `multi_dataset_batch_sampler`: round_robin
-
-#### All Hyperparameters
-<details><summary>Click to expand</summary>
-
-- `overwrite_output_dir`: False
-- `do_predict`: False
-- `eval_strategy`: steps
-- `prediction_loss_only`: True
-- `per_device_train_batch_size`: 64
-- `per_device_eval_batch_size`: 64
-- `per_gpu_train_batch_size`: None
-- `per_gpu_eval_batch_size`: None
-- `gradient_accumulation_steps`: 1
-- `eval_accumulation_steps`: None
-- `torch_empty_cache_steps`: None
-- `learning_rate`: 5e-05
-- `weight_decay`: 0.0
-- `adam_beta1`: 0.9
-- `adam_beta2`: 0.999
-- `adam_epsilon`: 1e-08
-- `max_grad_norm`: 1
-- `num_train_epochs`: 20
-- `max_steps`: -1
-- `lr_scheduler_type`: linear
-- `lr_scheduler_kwargs`: {}
-- `warmup_ratio`: 0.0
-- `warmup_steps`: 0
-- `log_level`: passive
-- `log_level_replica`: warning
-- `log_on_each_node`: True
-- `logging_nan_inf_filter`: True
-- `save_safetensors`: True
-- `save_on_each_node`: False
-- `save_only_model`: False
-- `restore_callback_states_from_checkpoint`: False
-- `no_cuda`: False
-- `use_cpu`: False
-- `use_mps_device`: False
-- `seed`: 42
-- `data_seed`: None
-- `jit_mode_eval`: False
-- `use_ipex`: False
-- `bf16`: False
-- `fp16`: False
-- `fp16_opt_level`: O1
-- `half_precision_backend`: auto
-- `bf16_full_eval`: False
-- `fp16_full_eval`: False
-- `tf32`: None
-- `local_rank`: 0
-- `ddp_backend`: None
-- `tpu_num_cores`: None
-- `tpu_metrics_debug`: False
-- `debug`: []
-- `dataloader_drop_last`: False
-- `dataloader_num_workers`: 0
-- `dataloader_prefetch_factor`: None
-- `past_index`: -1
-- `disable_tqdm`: False
-- `remove_unused_columns`: True
-- `label_names`: None
-- `load_best_model_at_end`: False
-- `ignore_data_skip`: False
-- `fsdp`: []
-- `fsdp_min_num_params`: 0
-- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
-- `fsdp_transformer_layer_cls_to_wrap`: None
-- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
-- `deepspeed`: None
-- `label_smoothing_factor`: 0.0
-- `optim`: adamw_torch
-- `optim_args`: None
-- `adafactor`: False
-- `group_by_length`: False
-- `length_column_name`: length
-- `ddp_find_unused_parameters`: None
-- `ddp_bucket_cap_mb`: None
-- `ddp_broadcast_buffers`: False
-- `dataloader_pin_memory`: True
-- `dataloader_persistent_workers`: False
-- `skip_memory_metrics`: True
-- `use_legacy_prediction_loop`: False
-- `push_to_hub`: False
-- `resume_from_checkpoint`: None
-- `hub_model_id`: None
-- `hub_strategy`: every_save
-- `hub_private_repo`: False
-- `hub_always_push`: False
-- `gradient_checkpointing`: False
-- `gradient_checkpointing_kwargs`: None
-- `include_inputs_for_metrics`: False
-- `eval_do_concat_batches`: True
-- `fp16_backend`: auto
-- `push_to_hub_model_id`: None
-- `push_to_hub_organization`: None
-- `mp_parameters`: 
-- `auto_find_batch_size`: False
-- `full_determinism`: False
-- `torchdynamo`: None
-- `ray_scope`: last
-- `ddp_timeout`: 1800
-- `torch_compile`: False
-- `torch_compile_backend`: None
-- `torch_compile_mode`: None
-- `dispatch_batches`: None
-- `split_batches`: None
-- `include_tokens_per_second`: False
-- `include_num_input_tokens_seen`: False
-- `neftune_noise_alpha`: None
-- `optim_target_modules`: None
-- `batch_eval_metrics`: False
-- `eval_on_start`: False
-- `eval_use_gather_object`: False
-- `batch_sampler`: batch_sampler
-- `multi_dataset_batch_sampler`: round_robin
-
-</details>
-
-### Training Logs
-| Epoch   | Step | Training Loss | negative_mse |
-|:-------:|:----:|:-------------:|:------------:|
-| 0.5089  | 200  | -             | -0.3720      |
-| 1.0     | 393  | -             | -0.3428      |
-| 1.0178  | 400  | -             | -0.3437      |
-| 1.2723  | 500  | 0.0024        | -            |
-| 1.5267  | 600  | -             | -0.3262      |
-| 2.0     | 786  | -             | -0.3153      |
-| 2.0356  | 800  | -             | -0.3156      |
-| 2.5445  | 1000 | 0.0018        | -0.3070      |
-| 3.0     | 1179 | -             | -0.3004      |
-| 3.0534  | 1200 | -             | -0.3005      |
-| 3.5623  | 1400 | -             | -0.2959      |
-| 3.8168  | 1500 | 0.0015        | -            |
-| 4.0     | 1572 | -             | -0.2907      |
-| 4.0712  | 1600 | -             | -0.2924      |
-| 4.5802  | 1800 | -             | -0.2863      |
-| 5.0     | 1965 | -             | -0.2831      |
-| 5.0891  | 2000 | 0.0013        | -0.2841      |
-| 5.5980  | 2200 | -             | -0.2792      |
-| 6.0     | 2358 | -             | -0.2765      |
-| 6.1069  | 2400 | -             | -0.2774      |
-| 6.3613  | 2500 | 0.0012        | -            |
-| 6.6158  | 2600 | -             | -0.2734      |
-| 7.0     | 2751 | -             | -0.2716      |
-| 7.1247  | 2800 | -             | -0.2722      |
-| 7.6336  | 3000 | 0.0011        | -0.2700      |
-| 8.0     | 3144 | -             | -0.2684      |
-| 8.1425  | 3200 | -             | -0.2683      |
-| 8.6514  | 3400 | -             | -0.2665      |
-| 8.9059  | 3500 | 0.001         | -            |
-| 9.0     | 3537 | -             | -0.2645      |
-| 9.1603  | 3600 | -             | -0.2649      |
-| 9.6692  | 3800 | -             | -0.2639      |
-| 10.0    | 3930 | -             | -0.2625      |
-| 10.1781 | 4000 | 0.0009        | -0.2619      |
-| 10.6870 | 4200 | -             | -0.2615      |
-| 11.0    | 4323 | -             | -0.2594      |
-| 11.1959 | 4400 | -             | -0.2598      |
-| 11.4504 | 4500 | 0.0009        | -            |
-| 11.7048 | 4600 | -             | -0.2587      |
-| 12.0    | 4716 | -             | -0.2582      |
-| 12.2137 | 4800 | -             | -0.2586      |
-| 12.7226 | 5000 | 0.0008        | -0.2573      |
-| 13.0    | 5109 | -             | -0.2568      |
-| 13.2316 | 5200 | -             | -0.2567      |
-| 13.7405 | 5400 | -             | -0.2564      |
-| 13.9949 | 5500 | 0.0008        | -            |
-| 14.0    | 5502 | -             | -0.2558      |
-| 14.2494 | 5600 | -             | -0.2560      |
-| 14.7583 | 5800 | -             | -0.2551      |
-| 15.0    | 5895 | -             | -0.2548      |
-| 15.2672 | 6000 | 0.0008        | -0.2552      |
-| 15.7761 | 6200 | -             | -0.2540      |
-| 16.0    | 6288 | -             | -0.2534      |
-| 16.2850 | 6400 | -             | -0.2538      |
-| 16.5394 | 6500 | 0.0008        | -            |
-| 16.7939 | 6600 | -             | -0.2529      |
-| 17.0    | 6681 | -             | -0.2532      |
-| 17.3028 | 6800 | -             | -0.2530      |
-| 17.8117 | 7000 | 0.0008        | -0.2528      |
-| 18.0    | 7074 | -             | -0.2525      |
-| 18.3206 | 7200 | -             | -0.2527      |
-| 18.8295 | 7400 | -             | -0.2521      |
-
-
-### Framework Versions
-- Python: 3.10.14
-- Sentence Transformers: 3.1.1
-- Transformers: 4.44.2
-- PyTorch: 2.4.0
-- Accelerate: 0.34.2
-- Datasets: 3.0.0
-- Tokenizers: 0.19.1
-
-## Citation
-
-### BibTeX
-
-#### Sentence Transformers
-```bibtex
-@inproceedings{reimers-2019-sentence-bert,
-    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
-    author = "Reimers, Nils and Gurevych, Iryna",
-    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
-    month = "11",
-    year = "2019",
-    publisher = "Association for Computational Linguistics",
-    url = "https://arxiv.org/abs/1908.10084",
-}
-```
-
-#### MSELoss
-```bibtex
-@inproceedings{reimers-2020-multilingual-sentence-bert,
-    title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
-    author = "Reimers, Nils and Gurevych, Iryna",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
-    month = "11",
-    year = "2020",
-    publisher = "Association for Computational Linguistics",
-    url = "https://arxiv.org/abs/2004.09813",
-}
-```
-
-<!--
-## Glossary
-
-*Clearly define terms in order to be accessible across audiences.*
--->
-
-<!--
-## Model Card Authors
-
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
-
-<!--
-## Model Card Contact
-
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->
+Extended-mpnet